An automated dual-module AI-based solution for early detection and classification of crop diseases and stress conditions

One of the most important challenges faced by smallholder farmers in the agricultural industry is the lack of accurate, timely knowledge to predict and detect crop health issues. Crop productivity is often threatened not only by diseases but also by environmental and physiological stresses, which contribute to significant yield losses and negatively impact the national economy. Traditional detection methods are time-consuming, costly, and require expert knowledge, creating a need for automated and intelligent systems. This work proposes a dual-functional framework that combines crop disease and stress detection using advanced machine learning and deep learning techniques to accurately classify healthy and diseased leaves. This integrated system ensures early detection, reduces crop loss, improves productivity, and provides a scalable, farmer-friendly solution for sustainable agriculture.